A New Process to Tackle Misinformation on Social Media: Prevalence-Based Gradation

While misinformation and disinformation is not a new threat, it is accelerated by social media platforms. High-stakes information like election-related information, health-related information, etc., has critical consequences on individuals and communities in real life, but it is muddled with mis/disinformation. Platforms use various technological measures and predictive machine learning tools to detect unlawful content like child sexual images, pornography, dis/misinformation. These technological measures have their merits to an extent, especially where platforms can act faster and at scale. At the same time, we increasingly see content falling through the crack due to false negatives and getting struck or taken down due to false positives.

One of the critical reasons social media posts fall through the cracks is that platforms are presently confined to content-level intervention in the absence of process-level clarity and intervention within the content moderation pipeline. This lack of process-level intervention causes platforms to utilise resources and time inefficiently.

Against this backdrop, in this talk, I discuss/propose a novel process-level intervention that would refine the content moderation pipeline and enable the efficient use of tools and resources across its entirety. I propose a “prevalence-based gradation process” (PBG) – a system that uses prevalence as an integral element for hard moderation to tackle mis/disinformation. The talk will also show how the PBG process would act as a means through which social media platforms can evaluate content using ex-ante measures and exercise optimal corrective action in a calibrated format adjusted according to the exposure level of the information.